Sign inGet started
Paper · arXiv

The GRADIEND Python Package: An End-to-End System for Gradient-Based Feature Learning

We present gradiend, an open-source Python package that operationalizes the GRADIEND method for learning feature directions from factual-counterfactual MLM and CLM gradients in language models. The package provides a unified workflow for feature-related data creation, training, evaluation, visualization, persistent model rewriting via controlled weight updates, and multi-feature comparison. We demonstrate gradiend through an English pronoun running example, a semantic sentiment use case that evaluates lexical generalization to held-out target words, and a large-scale feature comparison.

AuthorsJonathan Drechsel, Steffen Herbold
Primary categorycs.CL
Published27 February 2026
Last updated at source6 September 2026
cs.CL
Embed this entry
<iframe src="https://silytics.si/embed/item/2cfe9399-ee14-405c-b26a-b8671d024045" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Free, no tracking. More about feeds and embeds.

Popularity, last 3 days

0 now. Counts as reported by the source, snapshotted daily by Silytics.

Connected in the knowledge graph

Explore this entry in the graph

Models and datasets referencing this paper

Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 20 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.